Phone Bluetooth¶

Sensor parameters description for [PHONE_BLUETOOTH]:

Key	Description
`[CONTAINER]`	Data stream container (database table, CSV file, etc.) where the bluetooth data is stored

RAPIDS provider¶

Warning

The features of this provider are deprecated in favor of DORYAB provider (see below).

Available time segments and platforms

Available for all time segments
Available for Android only

File Sequence

- data/raw/{pid}/phone_bluetooth_raw.csv
- data/raw/{pid}/phone_bluetooth_with_datetime.csv
- data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv
- data/processed/features/{pid}/phone_bluetooth.csv"

Parameters description for [PHONE_BLUETOOTH][PROVIDERS][RAPIDS]:

Key	Description
`[COMPUTE]`	Set to `True` to extract `PHONE_BLUETOOTH` features from the `RAPIDS` provider
`[FEATURES]`	Features to be computed, see table below

Features description for [PHONE_BLUETOOTH][PROVIDERS][RAPIDS]:

Feature	Units	Description
~~countscans~~	devices	Number of scanned devices during a time segment, a device can be detected multiple times over time and these appearances are counted separately
~~uniquedevices~~	devices	Number of unique devices during a time segment as identified by their hardware (`bt_address`) address
~~countscansmostuniquedevice~~	scans	Number of scans of the most sensed device within each time segment instance

Assumptions/Observations

From v0.2.0 countscans, uniquedevices, countscansmostuniquedevice were deprecated because they overlap with the respective features for ALL devices of the PHONE_BLUETOOTH DORYAB provider

DORYAB provider¶

This provider is adapted from the work by Doryab et al.

Available time segments and platforms

Available for all time segments
Available for Android only

File Sequence

- data/raw/{pid}/phone_bluetooth_raw.csv
- data/raw/{pid}/phone_bluetooth_with_datetime.csv
- data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv
- data/processed/features/{pid}/phone_bluetooth.csv"

Parameters description for [PHONE_BLUETOOTH][PROVIDERS][DORYAB]:

Key	Description
`[COMPUTE]`	Set to `True` to extract `PHONE_BLUETOOTH` features from the `DORYAB` provider
`[FEATURES]`	Features to be computed, see table below. These features are computed for three device categories: `all` devices, `own` devices and `other` devices.

Features description for [PHONE_BLUETOOTH][PROVIDERS][DORYAB]:

Feature	Units	Description
countscans	scans	Number of scans (rows) from the devices sensed during a time segment instance. The more scans a bluetooth device has the longer it remained within range of the participant’s phone
uniquedevices	devices	Number of unique bluetooth devices sensed during a time segment instance as identified by their hardware addresses (`bt_address`)
meanscans	scans	Mean of the scans of every sensed device within each time segment instance
stdscans	scans	Standard deviation of the scans of every sensed device within each time segment instance
countscansmostfrequentdevicewithinsegments	scans	Number of scans of the most sensed device within each time segment instance
countscansleastfrequentdevicewithinsegments	scans	Number of scans of the least sensed device within each time segment instance
countscansmostfrequentdeviceacrosssegments	scans	Number of scans of the most sensed device across time segment instances of the same type
countscansleastfrequentdeviceacrosssegments	scans	Number of scans of the least sensed device across time segment instances of the same type per device
countscansmostfrequentdeviceacrossdataset	scans	Number of scans of the most sensed device across the entire dataset of every participant
countscansleastfrequentdeviceacrossdataset	scans	Number of scans of the least sensed device across the entire dataset of every participant

Assumptions/Observations

Devices are classified as belonging to the participant (own) or to other people (others) using k-means based on the number of times and the number of days each device was detected across each participant’s dataset. See Doryab et al for more details.
If ownership cannot be computed because all devices were detected on only one day, they are all considered as other. Thus all and other features will be equal. The likelihood of this scenario decreases the more days of data you have.
When searching for the most frequent device across 30-minute segments, the search range is equivalent to the sum of all segments of the same time period. For instance, the countscansmostfrequentdeviceacrosssegments for the time segment (Fri 00:00:00, Fri 00:29:59) will get the count in that segment of the most frequent device found within all (00:00:00, 00:29:59) time segments. To find countscansmostfrequentdeviceacrosssegments for other devices, the search range needs to filter out all own devices. But no need to do so for countscansmostfrequentdeviceacrosssedataset. The most frequent device across the dataset stays the same for countscansmostfrequentdeviceacrossdatasetall, countscansmostfrequentdeviceacrossdatasetown and countscansmostfrequentdeviceacrossdatasetother. Same rule applies to the least frequent device across the dataset.
The most and least frequent devices will be the same across time segment instances and across the entire dataset when every time segment instance covers every hour of a dataset. For example, daily segments (00:00 to 23:59) fall in this category but morning segments (06:00am to 11:59am) or periodic 30-minute segments don’t.

Example

Simplified raw bluetooth data

The following is a simplified example with bluetooth data from three days and two time segments: morning and afternoon. There are two own devices: 5C836F5-487E-405F-8E28-21DBD40FA4FF detected seven times across two days and 499A1EAF-DDF1-4657-986C-EA5032104448 detected eight times on a single day.

local_date  segment     bt_address                              own_device
2016-11-29  morning     55C836F5-487E-405F-8E28-21DBD40FA4FF              1
2016-11-29  morning     55C836F5-487E-405F-8E28-21DBD40FA4FF              1
2016-11-29  morning     55C836F5-487E-405F-8E28-21DBD40FA4FF              1
2016-11-29  morning     55C836F5-487E-405F-8E28-21DBD40FA4FF              1
2016-11-29  morning     48872A52-68DE-420D-98DA-73339A1C4685              0
2016-11-29  afternoon   55C836F5-487E-405F-8E28-21DBD40FA4FF              1
2016-11-29  afternoon   48872A52-68DE-420D-98DA-73339A1C4685              0
2016-11-30  morning     55C836F5-487E-405F-8E28-21DBD40FA4FF              1
2016-11-30  morning     48872A52-68DE-420D-98DA-73339A1C4685              0
2016-11-30  morning     25262DC7-780C-4AD5-AD3A-D9776AEF7FC1              0
2016-11-30  morning     5B1E6981-2E50-4D9A-99D8-67AED430C5A8              0
2016-11-30  morning     5B1E6981-2E50-4D9A-99D8-67AED430C5A8              0
2016-11-30  afternoon   55C836F5-487E-405F-8E28-21DBD40FA4FF              1
2017-05-07  morning     5C5A9C41-2F68-4CEB-96D0-77DE3729B729              0
2017-05-07  morning     25262DC7-780C-4AD5-AD3A-D9776AEF7FC1              0
2017-05-07  morning     5B1E6981-2E50-4D9A-99D8-67AED430C5A8              0
2017-05-07  morning     6C444841-FE64-4375-BC3F-FA410CDC0AC7              0
2017-05-07  morning     4DC7A22D-9F1F-4DEF-8576-086910AABCB5              0
2017-05-07  afternoon   5B1E6981-2E50-4D9A-99D8-67AED430C5A8              0
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1
2017-05-07  afternoon   499A1EAF-DDF1-4657-986C-EA5032104448              1

The most and least frequent OTHER devices (own_device == 0) during morning segments

The most and least frequent ALL|OWN|OTHER devices are computed within each time segment instance, across time segment instances of the same type and across the entire dataset of each person. These are the most and least frequent devices for OTHER devices during morning segments.

most frequent device across 2016-11-29 morning:   '48872A52-68DE-420D-98DA-73339A1C4685'  (this device is the only one in this instance)
least frequent device across 2016-11-29 morning:  '48872A52-68DE-420D-98DA-73339A1C4685'  (this device is the only one in this instance)
most frequent device across 2016-11-30 morning:   '5B1E6981-2E50-4D9A-99D8-67AED430C5A8'
least frequent device across 2016-11-30 morning:  '25262DC7-780C-4AD5-AD3A-D9776AEF7FC1'  (when tied, the first occurance is chosen)
most frequent device across 2017-05-07 morning:   '25262DC7-780C-4AD5-AD3A-D9776AEF7FC1'  (when tied, the first occurance is chosen)
least frequent device across 2017-05-07 morning:  '25262DC7-780C-4AD5-AD3A-D9776AEF7FC1'  (when tied, the first occurance is chosen)

most frequent across morning segments:            '5B1E6981-2E50-4D9A-99D8-67AED430C5A8'
least frequent across morning segments:           '6C444841-FE64-4375-BC3F-FA410CDC0AC7' (when tied, the first occurance is chosen)

most frequent across dataset:                     '499A1EAF-DDF1-4657-986C-EA5032104448' (only taking into account "morning" segments)
least frequent across dataset:                    '4DC7A22D-9F1F-4DEF-8576-086910AABCB5' (when tied, the first occurance is chosen)

Bluetooth features for OTHER devices and morning segments

For brevity we only show the following features for morning segments:

OTHER: 
    DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
    SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]

Note that countscansmostfrequentdeviceacrossdatasetothers is all 0s because 499A1EAF-DDF1-4657-986C-EA5032104448 is excluded from the count as is labelled as an own device (not other).

local_segment       countscansothers    uniquedevicesothers meanscansothers stdscansothers  countscansmostfrequentdevicewithinsegmentsothers    countscansmostfrequentdeviceacrosssegmentsothers    countscansmostfrequentdeviceacrossdatasetothers
2016-11-29-morning  1                   1                   1.000000        NaN             1                                                   0.0                                                 0.0
2016-11-30-morning  4                   3                   1.333333        0.57735         2                                                   2.0                                                 2.0
2017-05-07-morning  5                   5                   1.000000        0.00000         1                                                   1.0                                                 1.0